Search results for "Canopy clustering algorithm"

showing 10 items of 12 documents

Fast dendrogram-based OTU clustering using sequence embedding

2014

Biodiversity assessment is an important step in a metagenomic processing pipeline. The biodiversity of a microbial metagenome is often estimated by grouping its 16S rRNA reads into operational taxonomic units or OTUs. These metagenomic datasets are typically large and hence require effective yet accurate computational methods for processing.In this paper, we introduce a new hierarchical clustering method called CRiSPy-Embed which aims to produce high-quality clustering results at a low computational cost. We tackle two computational issues of the current OTU hierarchical clustering approach: (1) the compute-intensive sequence alignment operation for building the distance matrix and (2) the …

Brown clusteringCURE data clustering algorithmSingle-linkage clusteringCorrelation clusteringCanopy clustering algorithmData miningBiologyHierarchical clustering of networksCluster analysiscomputer.software_genrecomputerHierarchical clusteringProceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
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SMART: Unique splitting-while-merging framework for gene clustering

2014

© 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named "splitting merging awareness tactics" (SMART), which does not require any a priori knowledge of either the number …

Clustering algorithmsMicroarrayslcsh:MedicineGene ExpressionBioinformaticscomputer.software_genreCell SignalingData MiningCluster Analysislcsh:ScienceFinite mixture modelOligonucleotide Array Sequence AnalysisPhysicsMultidisciplinarySMART frameworkConstrained clusteringCompetitive learning modelBioassays and Physiological AnalysisMultigene FamilyCanopy clustering algorithmEngineering and TechnologyData miningInformation TechnologyGenomic Signal ProcessingAlgorithmsResearch ArticleSignal TransductionComputer and Information SciencesFuzzy clusteringCorrelation clusteringResearch and Analysis MethodsClusteringMolecular GeneticsCURE data clustering algorithmGeneticsGene RegulationCluster analysista113Gene Expression Profilinglcsh:RBiology and Life SciencesComputational BiologyCell BiologyDetermining the number of clusters in a data setComputingMethodologies_PATTERNRECOGNITIONSplitting-merging awareness tactics (SMART)Signal ProcessingAffinity propagationlcsh:QGene expressionClustering frameworkcomputer
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Data Analysis and Bioinformatics

2007

Data analysis methods and techniques are revisited in the case of biological data sets. Particular emphasis is given to clustering and mining issues. Clustering is still a subject of active research in several fields such as statistics, pattern recognition, and machine learning. Data mining adds to clustering the complications of very large data-sets with many attributes of different types. And this is a typical situation in biology. Some cases studies are also described.

Clustering high-dimensional dataFuzzy clusteringComputer sciencebusiness.industryCorrelation clusteringConceptual clusteringMachine learningcomputer.software_genreComputingMethodologies_PATTERNRECOGNITIONCURE data clustering algorithmConsensus clusteringCanopy clustering algorithmData miningArtificial intelligenceCluster analysisbusinesscomputer
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Structural clustering of millions of molecular graphs

2014

We propose an algorithm for clustering very large molecular graph databases according to scaffolds (i.e., large structural overlaps) that are common between cluster members. Our approach first partitions the original dataset into several smaller datasets using a greedy clustering approach named APreClus based on dynamic seed clustering. APreClus is an online and instance incremental clustering algorithm delaying the final cluster assignment of an instance until one of the so-called pending clusters the instance belongs to has reached significant size and is converted to a fixed cluster. Once a cluster is fixed, APreClus recalculates the cluster centers, which are used as representatives for…

Clustering high-dimensional dataFuzzy clusteringTheoretical computer sciencek-medoidsComputer scienceSingle-linkage clusteringCorrelation clusteringConstrained clusteringcomputer.software_genreComplete-linkage clusteringGraphHierarchical clusteringComputingMethodologies_PATTERNRECOGNITIONData stream clusteringCURE data clustering algorithmCanopy clustering algorithmFLAME clusteringAffinity propagationData miningCluster analysiscomputerk-medians clusteringClustering coefficientProceedings of the 29th Annual ACM Symposium on Applied Computing
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Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm

2003

A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the benefits of the plain clustering algorithm with regard to other approaches for clustering. Experiments using both synthetic and real data have been performed in order to evaluate the differences between the proposed methodology and the plain use of the Maximum Variance algorithm. According to the results obtained, the proposal constitutes an efficient and accurate alternative.

Clustering high-dimensional datak-medoidsComputer scienceCURE data clustering algorithmSingle-linkage clusteringCanopy clustering algorithmVariance (accounting)Data miningCluster analysiscomputer.software_genrecomputerk-medians clustering
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Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting.

2015

De novo clustering is a popular technique to perform taxonomic profiling of a microbial community by grouping 16S rRNA amplicon reads into operational taxonomic units (OTUs). In this work, we introduce a new dendrogram-based OTU clustering pipeline called CRiSPy. The key idea used in CRiSPy to improve clustering accuracy is the application of an anomaly detection technique to obtain a dynamic distance cutoff instead of using the de facto value of 97 percent sequence similarity as in most existing OTU clustering pipelines. This technique works by detecting an abrupt change in the merging heights of a dendrogram. To produce the output dendrograms, CRiSPy employs the OTU hierarchical clusterin…

Computer scienceCorrelation clusteringSingle-linkage clusteringMolecular Sequence DataMachine learningcomputer.software_genrePattern Recognition AutomatedCURE data clustering algorithmRNA Ribosomal 16SGeneticsComputer GraphicsCluster analysisBase Sequencebusiness.industryApplied MathematicsDendrogramHigh-Throughput Nucleotide SequencingPattern recognitionSignal Processing Computer-AssistedEquipment DesignHierarchical clusteringEquipment Failure AnalysisRNA BacterialCanopy clustering algorithmArtificial intelligenceHierarchical clustering of networksbusinesscomputerSequence AlignmentAlgorithmsBiotechnologyIEEE/ACM transactions on computational biology and bioinformatics
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Scalable Clustering by Iterative Partitioning and Point Attractor Representation

2016

Clustering very large datasets while preserving cluster quality remains a challenging data-mining task to date. In this paper, we propose an effective scalable clustering algorithm for large datasets that builds upon the concept of synchronization. Inherited from the powerful concept of synchronization, the proposed algorithm, CIPA (Clustering by Iterative Partitioning and Point Attractor Representations), is capable of handling very large datasets by iteratively partitioning them into thousands of subsets and clustering each subset separately. Using dynamic clustering by synchronization, each subset is then represented by a set of point attractors and outliers. Finally, CIPA identifies the…

Fuzzy clusteringGeneral Computer ScienceComputer scienceSingle-linkage clusteringCorrelation clusteringConstrained clustering02 engineering and technologycomputer.software_genreComputingMethodologies_PATTERNRECOGNITIONData stream clusteringCURE data clustering algorithm020204 information systems0202 electrical engineering electronic engineering information engineeringCanopy clustering algorithm020201 artificial intelligence & image processingData miningCluster analysiscomputerACM Transactions on Knowledge Discovery from Data
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A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm

2015

The problem of clustering, or unsupervised classification, has been solved by a myriad of techniques, all of which depend, either directly or implicitly, on the Bayesian principle of optimal classification. To be more specific, within a Bayesian paradigm, if one is to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the distance from the corresponding means or central points in the respective distributions. When this principle is applied in clustering, one would assign an unassigned sample into the cluster whose mean is the closest, and this can be done in either a bottom-up or a top-dow…

Fuzzy clusteringbusiness.industryComputer scienceCorrelation clusteringConstrained clusteringPattern recognitioncomputer.software_genreData stream clusteringCURE data clustering algorithmCanopy clustering algorithmAffinity propagationArtificial intelligenceData miningbusinessCluster analysiscomputer
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SparseHC: A Memory-efficient Online Hierarchical Clustering Algorithm

2014

Computing a hierarchical clustering of objects from a pairwise distance matrix is an important algorithmic kernel in computational science. Since the storage of this matrix requires quadratic space with respect to the number of objects, the design of memory-efficient approaches is of high importance to this research area. In this paper, we address this problem by presenting a memory-efficient online hierarchical clustering algorithm called SparseHC. SparseHC scans a sorted and possibly sparse distance matrix chunk-by-chunk. Meanwhile, a dendrogram is built by merging cluster pairs as and when the distance between them is determined to be the smallest among all remaining cluster pairs. The k…

sparse matrixClustering high-dimensional dataTheoretical computer scienceonline algorithmsComputer scienceSingle-linkage clusteringComplete-linkage clusteringNearest-neighbor chain algorithmConsensus clusteringmemory-efficient clusteringCluster analysisk-medians clusteringGeneral Environmental ScienceSparse matrix:Engineering::Computer science and engineering [DRNTU]k-medoidsDendrogramConstrained clusteringHierarchical clusteringDistance matrixCanopy clustering algorithmGeneral Earth and Planetary SciencesFLAME clusteringHierarchical clustering of networkshierarchical clusteringAlgorithmProcedia Computer Science
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A novel heuristic memetic clustering algorithm

2013

In this paper we introduce a novel clustering algorithm based on the Memetic Algorithm meta-heuristic wherein clusters are iteratively evolved using a novel single operator employing a combination of heuristics. Several heuristics are described and employed for the three types of selections used in the operator. The algorithm was exhaustively tested on three benchmark problems and compared to a classical clustering algorithm (k-Medoids) using the same performance metrics. The results show that our clustering algorithm consistently provides better clustering solutions with less computational effort.

ta113Determining the number of clusters in a data setBiclusteringClustering high-dimensional dataDBSCANComputingMethodologies_PATTERNRECOGNITIONTheoretical computer scienceCURE data clustering algorithmCorrelation clusteringCanopy clustering algorithmCluster analysisAlgorithmMathematics2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
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